arXiv subjects

P. Bernát Szabó

Publications and source records attributed to P. Bernát Szabó.

9 recordsLinked to original sources

arXiv2026

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking

Reliable description of bond breaking remains a major challenge for quantum chemistry due to the multireference character of the electronic structure in dissociating species. Multireference methods in particular suffer from large computational cost, which under the normal paradigm has to be paid anew for each system at a full price, ignoring commonalities in electronic structure across molecules. Quantum Monte Carlo with deep neural networks uniquely offers to exploit such commonalities by pretraining transferable wavefunction models, but all such attempts were so far limited in scope. Here, we bring this paradigm to fruition with Orbformer, a transferable wavefunction model pretrained on 22,000 equilibrium and dissociating structures that can be fine-tuned on unseen molecules reaching an accuracy-cost ratio rivalling classical multireference methods. On established benchmarks as well as more challenging bond dissociations and Diels-Alder reactions, Orbformer is the only method that consistently converges to chemical accuracy (1 kcal/mol). This work turns the idea of amortizing the cost of solving the Schrödinger equation over many molecules into a practical approach in quantum chemistry.

physics.chem-ph↗

arXiv2026

Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo

The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods. When entire ground and excited potential energy surfaces (PESs) are desired, for instance to predict the interaction of light excitation and structural changes, one is often forced to use cheaper computational methods at the cost of reduced accuracy. Here we introduce a method for the geometrically transferable optimization of neural network wave functions that leverages weight sharing and dynamical ordering of electronic states. Our method enables the efficient prediction of ground and excited-state PESs and their intersections at the highest accuracy, demonstrating up to two orders of magnitude cost reduction compared to single-point calculations.We validate our approach on four challenging excited-state PESs, namely ethylene, the carbon dimer, the methylenimmonium cation, and a rubredoxin active site model containing 96 electrons, illustrating the potential of transferable deep-learning QMC as a practical framework for studying electronic excitations in molecules.

physics.chem-ph↗

arXiv2026

Accurate and scalable exchange-correlation with deep learning

Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally limited by the need to approximate the unknown exchange-correlation (XC) functional. The traditional paradigm for improving accuracy has relied on increasingly elaborate hand-crafted functional forms. This approach has led to a longstanding trade-off between computational efficiency and accuracy, which remains insufficient for reliable predictive modelling of laboratory experiments. Here we introduce Skala, a deep learning-based XC functional that surpasses state-of-the-art hybrid functionals in accuracy across the main-group chemistry benchmark set GMTKN55 with an error of 2.8 kcal/mol, while retaining the lower computational cost characteristic of semi-local DFT. This demonstrated departure from the historical trade-off between accuracy and efficiency is enabled by learning non-local representations of electronic structure directly from data, bypassing the need for increasingly costly hand-engineered features. Leveraging an unprecedented volume of high-accuracy reference data from wavefunction-based methods, we establish that modern deep learning enables systematically improvable neural exchange-correlation models as training datasets expand, positioning first-principles simulations to become progressively more predictive.

physics.chem-ph↗

arXiv2026

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo

A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES), which remains challenging for strongly correlated systems. Transferable deep-learning variational Monte Carlo (VMC) offers a promising route by efficiently solving the electronic Schrödinger equation jointly across molecular geometries at consistently high accuracy, yet its stochastic nature renders direct exploration of molecular configuration space nontrivial. Here, we present a framework for highly accurate ab initio exploration of PESs that combines transferable deep-learning VMC with a cost-effective estimation of energies, forces, and Hessians. By continuously sampling nuclear configurations during VMC optimization of electronic wave functions, we obtain transferable descriptions that achieve zero-shot chemical accuracy within chemically relevant distributions of molecular geometries. Throughout the subsequent characterization of molecular configuration space, the PES is evaluated only sparsely, with local approximations constructed by estimating VMC energies and forces at sampled geometries and aggregating the resulting noisy data using Gaussian process regression. Our method enables accurate and efficient exploration of complex PES landscapes, including structure relaxation, transition-state searches, and minimum-energy pathways, for both ground and excited states. This opens the door to studying bond breaking, formation, and large structural rearrangements in systems with pronounced multi-reference character.

physics.chem-ph↗

arXiv2025

Partitioning the electronic wave function using deep variational Monte Carlo

We propose a novel wave function partitioning method that integrates deep-learning variational Monte Carlo with ansätze based on generalized product functions. This approach effectively separates electronic wave functions (WFs) into multiple partial WFs representing, for example, the core and valence domains or different electronic shells. Although our ansätze do not explicitly include correlations between individual electron groups, we show that they accurately reproduce the underlying physics and chemical properties, such as dissociation curve, dipole moment, reaction energy, ionization energy, or atomic sizes. We identify the optimal number of core electrons and define physical core sizes for Li to Mg atoms. Our results demonstrate that core electrons can be effectively decoupled from valence electrons. We show that the core part of the WF remains nearly constant across different molecules and their geometries, enabling the transfer and reuse of the core part in WFs of more complex systems. This work provides a general framework for WF decomposition, offering potential advantages in computing and studying larger systems, and possibly paving the way for ab-initio development of effective core potentials. Though currently limited to small molecules due to scaling, we highlight several directions for extending our method it to larger systems.

physics.chem-ph↗

arXiv2025

Deep quantum Monte Carlo approach for polaritonic chemistry

Recent years have witnessed a surge of experimental and theoretical interest in controlling the properties of matter, such as its chemical reactivity, by confining it in optical cavities, where the enhancement of the light-matter coupling strength leads to the creation of hybrid light-matter states known as polaritons. However, ab initio calculations that account for the quantum nature of both the electromagnetic field and matter are challenging and have only started to be developed in recent years. We introduce a deep learning variational quantum Monte Carlo method to solve the electronic and photonic Schrödinger equation of molecules trapped in optical cavities. We extend typical electronic neural network wavefunction ansatzes to describe joint fermionic and bosonic systems, i.e. electron-photon systems, in a quantum Monte Carlo framework. We apply our method to hydrogen molecules in a cavity, computing both ground and excited states. We assess their energy, dipole moment, charge density shift due to the cavity, the state of the photonic field, and the entanglement developed between the electrons and photons. When possible, we compare our results with more conventional quantum chemistry methods proposed in the literature, finding good qualitative agreement, thus extending the range of scientific problems that can be tackled using machine learning techniques.

physics.chem-ph↗

arXiv2024

Highly Accurate Real-space Electron Densities with Neural Networks

Variational ab-initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows in principle straightforward extraction of any other observable of interest, besides the energy, but in practice this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation. We use variational quantum Monte Carlo with deep-learning ansätze (deep QMC) to obtain highly accurate wave functions free of basis set errors, and from them, using our novel method, correspondingly accurate electron densities, which we demonstrate by calculating dipole moments, nuclear forces, contact densities, and other density-based properties.

physics.chem-ph↗

arXiv2024

An improved penalty-based excited-state variational Monte Carlo approach with deep-learning ansatzes

We introduce several improvements to the penalty-based variational quantum Monte Carlo (VMC) algorithm for computing electronic excited states of Entwistle $\textit{et al.}$ [M. T. Entwistle $\textit{et al.}$, Nat. Commun. $\textbf{14}$, 274 (2023)], and demonstrate that the accuracy of the updated method is competitive with other available excited-state VMC approaches. A theoretical comparison of the computational aspects of these algorithms is presented, where several benefits of the penalty-based method are identified. Our main contributions include an automatic mechanism for tuning the scale of the penalty terms, an updated form of the overlap penalty with proven convergence properties, and a new term that penalizes the spin of the wave function, enabling the selective computation of states with a given spin. With these improvements, along with the use of the latest self-attention-based ansatz, the penalty-based method achieves a mean absolute error below 1 kcal/mol for the vertical excitation energies of a set of 26 atoms and molecules, without relying on variance matching schemes. Considering excited states along the dissociation of the carbon dimer, the accuracy of the penalty-based method is on par with that of natural-excited-state (NES) VMC, while also providing results for larger sections of the potential energy surface. Additionally, the accuracy of the original penalty-based method is improved for a conical intersection of ethylene, with the predicted angle of the intersection agreeing well with both NES-VMC and multi-reference configuration interaction.

physics.chem-ph↗

arXiv2023

Linear-scaling local natural orbital CCSD(T) approach for open-shell systems: algorithm, benchmarks, and large-scale applications

The extension of the highly-optimized local natural orbital (LNO) CCSD(T) method is presented for high-spin open-shell molecules. The techniques enabling the outstanding efficiency of the closed-shell LNO-CCSD(T) variant are adopted, including the iteration- and redundancy-free MP2 and (T) formulations, as well as the integral-direct, memory- and disk use economic, and OpenMP-parallel algorithms. For large molecules, the efficiency of our open-shell LNO-CCSD(T) method approaches that of its closed-shell parent method due to a novel approximation for higher-order long-range spin-polarization effects. The accuracy of open-shell LNO-CCSD(T) is extensively tested for radicals and reactions thereof, ionization processes, as well as spin-state splittings and transition-metal compounds. At the size range, where the canonical CCSD(T) reference is accessible (up to 20-30 atoms) the average open-shell LNO-CCSD(T) correlation energies are found to be 99.9-99.95% accurate, which translates into average absolute deviations of a few tenth of a kcal/mol in the investigated energy differences already with the default settings. This enables the accurate modeling of large systems with complex electronic structure, as illustrated on open-shell organic radicals and transition metal complexes of up to 179 atoms, as well as on challenging biochemical systems, including up to 601 atoms and 11,000 basis functions. While the protein models involve difficulties for local approximations, such as the spin states of a bounded iron ion or an extremely delocalized singly occupied orbital, the corresponding single-node LNO-CCSD(T) computations were feasible in a matter of days with 10s to a 100 GB of memory use. Therefore, the new LNO-CCSD(T) implementation enables highly-accurate computations for open-shell systems of unprecedented size and complexity with widely accessible hardware.

physics.chem-ph↗